This invention relates to a method for lighting- and view-angle-invariant face description with first- and second-order eigenfeatures, and can be used in face description and recognition for content-based image retrieval, huma face identification and verification for bank, security system, and video phone, surveillance and tracking, digital library, and internet multimedia database.
Human face perception is an active area in the computer vision community. Face recognition will play an important role in multimedia database search and many other applications. In recent years considerable progress has been made on the problems of face detection and recognition. Different specific techniques were proposed. Among these, neural nets, elastic template matching, Karhunen-Loeve expansion, algebraic moments and isodensity lines are typical methods.
Among these methods, Principal component analysis (PCA) or Karhunen-Loeve expansion is an important branch. Eigenface method is derived from PCA and it is convenient to be computed and has consistent accuracy in identification. Prior work showed that the PCA approach dissociates spontaneously between different types of information. The eigenvectors with large eigenvalues capture information that is common to subsets of faces and eigenvectors with small eigenvalues capture information specific to individual face. The studies show that only the information contained by the eigenvectors with large eigenvalues can be generalized to new faces that are not trained.
The advantage of eigenface method is that eigenvectors with large eigenvalues convey information relative to the basic shape and structure of the faces. That means features extracted from eigenvectors with large eigenvalues can be used to describe major characteristics of human faces. However, this is also the weakness of PCA. If we only consider the features extracted from eigenvectors with large eigenvalues, we cannot get the details of the faces which corresponds to individual faces. If these details of individual face can be described with the common features of human faces, the description of human faces can be more accurate.
Furthermore, it is difficult to get suitable eigenvectors to represent human faces if the face images are taken under different lighting conditions and different view-angles. According to our experience, the regular eigenface method is not excellent in recognizing faces under different lighting condition. However, many face images of the same person are taken under different lighting and view-angle conditions. The lighting-invariant and view-angle-invariant face description is still an important field to be explored.